CN114185976A - Visual intelligent perception platform of blast furnace - Google Patents
Visual intelligent perception platform of blast furnace Download PDFInfo
- Publication number
- CN114185976A CN114185976A CN202111281461.7A CN202111281461A CN114185976A CN 114185976 A CN114185976 A CN 114185976A CN 202111281461 A CN202111281461 A CN 202111281461A CN 114185976 A CN114185976 A CN 114185976A
- Authority
- CN
- China
- Prior art keywords
- blast furnace
- furnace
- intelligent
- module
- visualization
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 230000008447 perception Effects 0.000 title claims abstract description 15
- 230000000007 visual effect Effects 0.000 title claims abstract description 12
- 238000004519 manufacturing process Methods 0.000 claims abstract description 76
- 238000012800 visualization Methods 0.000 claims abstract description 74
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 30
- 238000007726 management method Methods 0.000 claims abstract description 13
- 238000013135 deep learning Methods 0.000 claims abstract description 9
- 238000005457 optimization Methods 0.000 claims abstract description 8
- 230000008859 change Effects 0.000 claims description 58
- 238000003745 diagnosis Methods 0.000 claims description 56
- 239000000463 material Substances 0.000 claims description 48
- 238000009826 distribution Methods 0.000 claims description 46
- 238000001514 detection method Methods 0.000 claims description 42
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims description 36
- 239000002893 slag Substances 0.000 claims description 29
- 238000004364 calculation method Methods 0.000 claims description 23
- 238000000034 method Methods 0.000 claims description 20
- 238000012544 monitoring process Methods 0.000 claims description 19
- 229910052742 iron Inorganic materials 0.000 claims description 18
- 230000033228 biological regulation Effects 0.000 claims description 16
- 239000000571 coke Substances 0.000 claims description 13
- 238000012545 processing Methods 0.000 claims description 12
- 238000012360 testing method Methods 0.000 claims description 12
- 230000000694 effects Effects 0.000 claims description 11
- 230000008569 process Effects 0.000 claims description 10
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 10
- 238000004891 communication Methods 0.000 claims description 8
- 230000006870 function Effects 0.000 claims description 8
- 238000010586 diagram Methods 0.000 claims description 7
- 238000007689 inspection Methods 0.000 claims description 7
- 230000009467 reduction Effects 0.000 claims description 7
- 239000003245 coal Substances 0.000 claims description 6
- 238000011084 recovery Methods 0.000 claims description 6
- 230000002159 abnormal effect Effects 0.000 claims description 5
- 238000002485 combustion reaction Methods 0.000 claims description 5
- 230000007797 corrosion Effects 0.000 claims description 5
- 238000005260 corrosion Methods 0.000 claims description 5
- 238000002347 injection Methods 0.000 claims description 5
- 239000007924 injection Substances 0.000 claims description 5
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 4
- 238000002844 melting Methods 0.000 claims description 4
- 230000008018 melting Effects 0.000 claims description 4
- 229910052760 oxygen Inorganic materials 0.000 claims description 4
- 239000001301 oxygen Substances 0.000 claims description 4
- 238000007781 pre-processing Methods 0.000 claims description 4
- 230000003993 interaction Effects 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 2
- 238000007405 data analysis Methods 0.000 abstract description 8
- 230000007246 mechanism Effects 0.000 abstract description 6
- 238000013499 data model Methods 0.000 abstract description 4
- 238000005516 engineering process Methods 0.000 abstract description 3
- 230000010354 integration Effects 0.000 abstract description 3
- 238000001816 cooling Methods 0.000 description 14
- 238000004458 analytical method Methods 0.000 description 13
- 238000011156 evaluation Methods 0.000 description 12
- 230000003628 erosive effect Effects 0.000 description 10
- 239000007789 gas Substances 0.000 description 8
- 238000013210 evaluation model Methods 0.000 description 6
- 239000000446 fuel Substances 0.000 description 6
- 238000007664 blowing Methods 0.000 description 5
- 239000003034 coal gas Substances 0.000 description 5
- 238000010079 rubber tapping Methods 0.000 description 4
- 238000009529 body temperature measurement Methods 0.000 description 3
- 230000003068 static effect Effects 0.000 description 3
- 238000012546 transfer Methods 0.000 description 3
- 229910000831 Steel Inorganic materials 0.000 description 2
- 239000011449 brick Substances 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 239000004615 ingredient Substances 0.000 description 2
- 239000007788 liquid Substances 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000012821 model calculation Methods 0.000 description 2
- 230000000630 rising effect Effects 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 239000010959 steel Substances 0.000 description 2
- 239000000725 suspension Substances 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 description 1
- 229910000805 Pig iron Inorganic materials 0.000 description 1
- 238000003723 Smelting Methods 0.000 description 1
- 210000001015 abdomen Anatomy 0.000 description 1
- 230000005856 abnormality Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 230000008034 disappearance Effects 0.000 description 1
- 239000004744 fabric Substances 0.000 description 1
- 229910052739 hydrogen Inorganic materials 0.000 description 1
- 239000001257 hydrogen Substances 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 230000024121 nodulation Effects 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 238000012827 research and development Methods 0.000 description 1
- 230000005236 sound signal Effects 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 229910052717 sulfur Inorganic materials 0.000 description 1
- 238000001931 thermography Methods 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
- G06F16/252—Integrating or interfacing systems involving database management systems between a Database Management System and a front-end application
-
- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21B—MANUFACTURE OF IRON OR STEEL
- C21B5/00—Making pig-iron in the blast furnace
- C21B5/006—Automatically controlling the process
-
- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21B—MANUFACTURE OF IRON OR STEEL
- C21B7/00—Blast furnaces
-
- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21B—MANUFACTURE OF IRON OR STEEL
- C21B7/00—Blast furnaces
- C21B7/24—Test rods or other checking devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/26—Visual data mining; Browsing structured data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
-
- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21B—MANUFACTURE OF IRON OR STEEL
- C21B2300/00—Process aspects
- C21B2300/04—Modeling of the process, e.g. for control purposes; CII
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Chemical & Material Sciences (AREA)
- Theoretical Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- Manufacturing & Machinery (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Materials Engineering (AREA)
- Economics (AREA)
- Organic Chemistry (AREA)
- Metallurgy (AREA)
- Tourism & Hospitality (AREA)
- Data Mining & Analysis (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- General Engineering & Computer Science (AREA)
- Development Economics (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Health & Medical Sciences (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Manufacture Of Iron (AREA)
Abstract
The invention relates to a visual intelligent perception platform of a blast furnace, which connects heterogeneous devices and collects data through intelligent modeling and visualization of complex industrial process operation dynamic performance combined by a big data analysis technology and a mechanism and intelligent optimization decision of management and production operation based on multi-target dynamic constraint, realizes edge or cloud computing and enhances the connection capacity of industrial internet platform devices through complete data acquisition integration, adopts some mathematical algorithms or theories, enhances the level data model and big data analysis capacity through a large amount of deep learning algorithm models, and enhances the safety guarantee of the devices.
Description
Technical Field
The invention relates to the field of industrial network platforms, in particular to a visual intelligent sensing platform for a blast furnace.
Background
Steel is an indispensable important resource in both floor construction and railway construction. There are basically two processes for manufacturing steel, one of which is important for producing pig iron, and blast furnace iron-making is the iron-making process mainly used in china. The existing blast furnace iron-making network platform needs high-level data models and big data analysis capability from industrial research and development, production, purchase, distribution, equipment management and the like, and how to connect heterogeneous equipment and collect data for intelligent coordination management becomes an urgent problem to be solved.
Disclosure of Invention
In order to solve the problem that the existing blast furnace iron-making Internet platform equipment has insufficient connection capacity and data analysis capacity, the invention provides a blast furnace visual intelligent sensing platform which comprises an application layer, a platform layer, an edge layer, an equipment layer and an MES (manufacturing execution system),
the equipment layer comprises a basic automation system (BA system) used for obtaining production adjustment instructions and adjusting a production plan according to the production adjustment instructions;
the edge layer comprises a data acquisition module and an edge calculation module, the data acquisition module is in communication connection with the MES system and the BA system respectively, the data acquisition module is used for acquiring blast furnace detection data, sending the production regulation and control instruction to the BA system and sending the production situation to the MES system, and the data acquisition module is also used for receiving production plan and inspection and test data sent by the MES system; the edge calculation module is used for acquiring blast furnace detection data needing image recognition in the blast furnace detection data and performing application preprocessing;
the MES system is used for acquiring blast furnace detection data and production reality of the database module, sending the production plan and the inspection and test data to the BA system, and correspondingly guiding and processing the production plan;
the platform layer comprises a data center module and a database module, wherein the data center module is used for storing blast furnace detection data, statistical data, intelligent model prediction results and blast furnace rule expert knowledge required by the platform, the database module comprises a time sequence database, a relation database and an unstructured database, the time sequence database is used for storing blast furnace real-time data, the relation database is used for storing blast furnace historical data, and the unstructured database is used for storing unstructured data in the blast furnace detection data;
the application layer comprises an intelligent model module, an intelligent diagnosis module, a knowledge reasoning module, a rule management module and a visualization module, wherein the intelligent model module is used for respectively establishing a plurality of intelligent algorithm models of furnace top material distribution, furnace body operation, tuyere and taphole change and furnace hearth state based on a deep learning algorithm; the intelligent diagnosis module establishes blast furnace expert rule knowledge according to the operation requirement of the blast furnace, provides corresponding operation guidance information according to the state prediction result and the blast furnace expert rule knowledge, and standardizes the production control of a blast furnace user according to the operation guidance information; the knowledge reasoning module is used for diagnosing and reasoning the state of the blast furnace according to the state prediction result in a plurality of preset state types to obtain an intelligent diagnosis result of the blast furnace; the rule management module is used for testing, building, editing and managing the blast furnace expert rule knowledge; the visualization module is used for performing visualization processing on the real-time state of the blast furnace and the data flow process of the blast furnace detection data.
On the basis of the scheme, the invention can also be improved as follows.
Further, the intelligent model module is used for respectively establishing a plurality of intelligent algorithm models of furnace top material distribution, furnace body operation, tuyere and taphole change and furnace hearth state based on a deep learning algorithm, and specifically comprises the following steps: and performing iterative training on the plurality of intelligent algorithm models through blast furnace detection data, performing optimization and parameter adjustment on the intelligent algorithm models according to a loss function, and performing multi-dimensional prediction on the state of the blast furnace through the plurality of intelligent algorithm models to obtain a state prediction result of the blast furnace.
Furthermore, the data acquisition module adopts an industrial gateway which is compatible with a plurality of access network modes and a bottom layer communication protocol; the data acquisition module is also used for acquiring intelligent monitoring signals acquired by the intelligent monitoring module.
Further, the visualization module is used for performing visualization processing on the real-time state of the blast furnace, and specifically comprises: the visualization module comprises a furnace charge visualization unit, an airflow visualization unit, a furnace type visualization unit, a furnace heat visualization unit and a safety visualization unit, and the visualization module respectively performs global visualization presentation on the real-time state of the blast furnace through a three-dimensional picture display mode from the blast furnace charge, the blast furnace airflow, the blast furnace type, the blast furnace heat and the blast furnace safety, and provides a corresponding visual interaction window.
Further, the furnace charge visualization unit is used for comprehensively displaying the charge surface shape and the charge batch position information of the whole period from the furnace top material distribution to the melting of the blast furnace in a graphical mode and monitoring the change of the furnace charge condition in real time;
the airflow visualization unit is used for visually presenting the distribution and strength change of the primary airflow, the secondary airflow and the top airflow of the blast furnace in a video stream mode;
the furnace type visualization unit is used for displaying the visualization graphs of the thermal load distribution, the shape of the reflow zone, the thickness distribution of the furnace type and the falling distribution of slag crust of the blast furnace by reconstructing the operation furnace type of the blast furnace in the modes of cloud pictures, thermodynamic diagrams and graphs;
the furnace heat visualization unit is used for displaying the combustion state of a blast furnace tuyere and the level of a furnace hearth thermal field, comprehensively displaying the heat distribution state in the blast furnace, and displaying the actual comparison condition of furnace temperature forecast and furnace temperature and the activity of the furnace hearth and iron slag balance data;
the safety visualization unit is used for alarming and prompting the running state and abnormal diagnosis of the blast furnace production, and the display content comprises water system leakage detection information, hearth corrosion state information, air port safety diagnosis information, distribution chute state diagnosis information and furnace top gear box state diagnosis information.
And the intelligent diagnosis module is further used for carrying out operation diagnosis on the production state of the blast furnace according to the data parameters of the blast furnace detection data and the state prediction result, and providing operation guidance information according to expert rule knowledge after carrying out feature extraction processing on the blast furnace detection data and carrying out identification operation on the production state.
Further, the state prediction result and the blast furnace expert rule knowledge of the intelligent diagnosis module provide corresponding operation guidance information to standardize the production control of a blast furnace user, and specifically, the operation guidance given by combining the diagnosis result is divided into operation adjustment and system adjustment, wherein the operation adjustment comprises air reduction adjustment, moisture adjustment, coal injection adjustment, oxygen enrichment adjustment, air reduction recovery and air temperature adjustment, and the system adjustment comprises coke ratio adjustment, additional coke adjustment, coke ratio recovery and charging system change.
And further, scoring the running state of the blast furnace from the dimensions of blast furnace burden, blast furnace airflow, blast furnace type, blast furnace heat and blast furnace safety respectively, and acquiring the comprehensive score of the whole running state of the blast furnace according to the corresponding weight of each dimension on the production influence of the blast furnace.
Further, the equipment layer further comprises an intelligent monitoring module, and the intelligent monitoring module acquires an intelligent monitoring signal through the camera equipment.
Further, the equipment layer further comprises an intelligent production module, and the intelligent production module comprises an unmanned trolley and an intelligent robot to carry out intelligent production.
The invention has the following beneficial effects: the heterogeneous devices are connected and data are collected through intelligent modeling and visualization of the operation dynamic performance of a complex industrial process combined by a big data analysis technology and a mechanism and intelligent optimization decision of management and production operation based on multi-target dynamic constraint, the connection capacity of the industrial internet platform device is enhanced through complete data acquisition and integration in edge or cloud computing, a plurality of mathematical algorithms or theories are adopted, a high-level data model and big data analysis capacity are improved through a large number of deep learning algorithm models, and meanwhile, the safety guarantee of the device is enhanced.
Drawings
FIG. 1 is a schematic structural diagram of a visual intelligent sensing platform of a blast furnace according to the invention;
FIG. 2 is a structural block diagram of a visual intelligent sensing platform of a blast furnace;
FIG. 3 is a schematic structural diagram of a visualization module according to the present invention;
FIG. 4 is a schematic diagram of the structure of the database according to the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1 and 2, the method for predicting the material level of the circulation network according to the embodiment of the present invention,
the invention provides a visual intelligent perception platform of a blast furnace, which comprises an application layer, a platform layer, an edge layer, an equipment layer and an MES (manufacturing execution system),
the equipment layer comprises a basic automation system (BA system) used for obtaining production adjustment instructions and adjusting a production plan according to the production adjustment instructions;
the edge layer comprises a data acquisition module and an edge calculation module, the data acquisition module is in communication connection with the MES system and the BA system respectively, the data acquisition module is used for acquiring blast furnace detection data, sending the production regulation and control instruction to the BA system and sending the production situation to the MES system, and the data acquisition module is also used for receiving production plan and inspection and test data sent by the MES system; the edge calculation module is used for acquiring blast furnace detection data needing image recognition in the blast furnace detection data and performing application preprocessing;
the MES system is used for acquiring blast furnace detection data and production reality of the database module, sending the production plan and the inspection and test data to the BA system, and correspondingly guiding and processing the production plan;
the platform layer comprises a data center module and a database module, wherein the data center module is used for storing blast furnace detection data, statistical data, intelligent model prediction results and blast furnace rule expert knowledge required by the platform, the database module comprises a time sequence database, a relation database and an unstructured database, the time sequence database is used for storing blast furnace real-time data, the relation database is used for storing blast furnace historical data, and the unstructured database is used for storing unstructured data in the blast furnace detection data;
the application layer comprises an intelligent model module, an intelligent diagnosis module, a knowledge reasoning module, a rule management module and a visualization module, wherein the intelligent model module is used for respectively establishing a plurality of intelligent algorithm models of furnace top material distribution, furnace body operation, tuyere and taphole change and furnace hearth state based on a deep learning algorithm; the intelligent diagnosis module establishes blast furnace expert rule knowledge according to the operation requirement of the blast furnace, provides corresponding operation guidance information according to the state prediction result and the blast furnace expert rule knowledge, and standardizes the production control of a blast furnace user according to the operation guidance information; the knowledge reasoning module is used for diagnosing and reasoning the state of the blast furnace according to the state prediction result in a plurality of preset state types to obtain an intelligent diagnosis result of the blast furnace; the rule management module is used for testing, building, editing and managing the blast furnace expert rule knowledge; the visualization module is used for performing visualization processing on the real-time state of the blast furnace and the data flow process of the blast furnace detection data.
According to the invention, heterogeneous devices are connected and data are collected through intelligent modeling and visualization of complex industrial process operation dynamic performance combined with a big data analysis technology and a mechanism, and intelligent optimization decision of management and production operation based on multi-target dynamic constraint, so that the connection capacity of industrial internet platform devices is enhanced through complete data acquisition integration in edge or cloud computing, a certain mathematical algorithm or theory is adopted, a high-level data model and big data analysis capacity are improved through a large amount of deep learning algorithm models, and meanwhile, the security of the devices is enhanced.
In this embodiment, as shown in fig. 1, the data acquisition module is in communication connection with the MES system and the BA system, respectively, and is responsible for data communication between the MES system and the BA system. The data acquisition module is used for acquiring blast furnace detection data, including raw fuel data, hot blast furnace data, blast furnace body data, blast furnace feeding data, under-tank weighing data, tapping and slag tapping data, pulverized coal injection data, molten iron temperature and weight data, energy medium data and the like from the BA system, and production adjustment instructions such as a batching plan, a distributing matrix, an air quantity and air pressure set value and the like are issued to the BA system; the method comprises the steps of receiving production plan and inspection and test data from an MES system, uploading production actual results to the MES system, wherein an edge calculation module is used for acquiring blast furnace detection data needing image recognition in the blast furnace detection data and carrying out application preprocessing, and a data acquisition industrial gateway has the compatibility with various access network modes and underlying communication protocols. Data acquisition should possess the function of gathering the required intelligent monitoring signal of intelligent perception. The video signal access and processing is preferably performed using a computing device having a separate GPU edge.
In this embodiment, the data center module is specifically configured to store blast furnace detection data, statistical data, an intelligent model prediction result, and blast furnace rule expert knowledge required by the platform, store functions of detection data, statistical data, a model result, and expert knowledge required by the system, and meet requirements of operation and storage of first-generation furnace age data of the blast furnace through data center hardware configuration, and has an online redundant hot standby function. As shown in fig. 3, the database module includes a time sequence database, a relational database and an unstructured database, the time sequence database is used for storing real-time data of the blast furnace, the relational database is used for storing historical data of the blast furnace, and the unstructured database is used for storing unstructured data in the detection data of the blast furnace. The platform layer is configured with a time sequence database, a relation database and an unstructured database; for storing real-time data, historical data and the inclusion of image, video, audio signals, respectively.
In this embodiment, the intelligent model module is used for establishing a plurality of intelligent algorithm model intelligent models of furnace top material distribution, furnace body operation, tuyere and taphole change and furnace hearth state respectively by a deep learning algorithm, and is based on the detection data, and is preferably adapted to adopt an intelligent algorithm by combining the intelligent monitoring signal to establish a process model associated with the furnace top material distribution, the furnace body operation, the tuyere and taphole change and the furnace hearth state. And performing iterative training on the plurality of intelligent algorithm models through blast furnace detection data, performing optimization and parameter adjustment on the intelligent algorithm models according to a loss function, and performing multi-dimensional measurement through the plurality of intelligent algorithm models to obtain a state prediction result of the blast furnace.
In this embodiment, specifically, the furnace top material distribution simulation model in the intelligent model calculates the material flow trajectory and the material level shape of each batch of material according to the material distribution actual performance data, obtains information such as the shape of the material level of the blast furnace, the radial O/C ratio distribution, and the like, and simulates the material distribution effect of 3-5 batches of material. The theoretical calculation is corrected by using the material distribution test data before blowing in the furnace, so that the calculation precision of the model is improved. According to the method, the mathematical model with the online self-learning function is adopted, and the model calculation result can be corrected online in real time according to the charge level shape scanned online, so that the accuracy of the model is improved.
The furnace burden tracking model calculates the actual iron content and coke ratio of each batch according to the actual weight of the batch and in combination with the ore grade of the ingredients of the batch, and further calculates the position (calculated in the smelting period) and the dynamic fuel ratio of each batch in the height direction in the furnace after the batch is fed into the furnace in real time according to the information of the stockline and the batch number to form a mirror image of the position and the shape information of the batch in the furnace, so as to provide a basis for determining the matched coal injection amount and the target furnace temperature in the charging process of the stock column.
The furnace top gas flow evaluation model is used for judging the distribution condition of the furnace top gas flow according to the related detection information of the furnace top gas flow. The diagnosis content comprises the strength, position information and variation trend of the central air flow, the middle air flow and the edge air flow, and provides basis for evaluating the cloth effect and adjusting the operation rule. Preferably, the cross temperature measurement data is used as basic data, and the comprehensive diagnosis of the airflow is carried out by determining the distribution of the airflow of the furnace top by combining the image recognition of the thermal imaging of the furnace top.
The in-furnace airflow evaluation model is used for evaluating the distribution strength and the variation trend of secondary airflow in a furnace body and assisting the judgment of pipelines, sliding materials and suspended materials according to the variation of the airflow. It is suitable to carry out comprehensive diagnosis according to the static pressure distribution of the furnace body, the temperature distribution of the cooling wall of the furnace body and the temperature variation trend of the cooling wall of the furnace body. The tuyere airflow evaluation model calculates the blowing kinetic energy, the convolution zone depth, the theoretical combustion temperature and the like of each tuyere in real time according to the size information of each tuyere, and provides the difference data of the tuyere kinetic energy to provide a basis for judging the active condition of the tuyere.
The operating furnace type thickness calculation model is used for calculating the thicknesses of brick linings and slag crust in different areas with different layer heights in the furnace in real time according to the structure and material characteristics of the blast furnace and diagnosing the thickness of the slag crust. The method is preferably used for calculating the inverse problem of heat transfer by taking the temperature of the cooling wall of the furnace body as a basic data and combining heat load water cooling temperature measurement information.
The slag crust falling evaluation model can judge the occurrence of captured slag crust falling and the thickness and area of each slag crust falling, and the judgment of the falling size needs to consider the association falling of adjacent cooling walls. And calculating a slag crust falling index according to the position, thickness and size information so as to represent the falling frequency and severity of the cooling walls of different sections in a period of time. The judgment of the falling of the slag crust is preferably based on the temperature change of the cooling wall.
And the operating furnace type analysis model correlates the production economic indicators in the same period according to the historical temperature data of the cooling walls of all sections of the blast furnace, and classifies and evaluates the historical operating furnace types. Therefore, the operating furnace type produced currently is classified and analyzed, the quality of the current furnace condition is judged, and a basis is provided for an operator to optimize the operating furnace type structure. The model is suitable for carrying out classification optimizing calculation on the operation furnace type by adopting a clustering algorithm and an optimization algorithm. The evaluation indexes of the operating furnace type comprise coke ratio, coal ratio, comprehensive coke ratio, air temperature, blast furnace utilization coefficient, air quantity, iron yield and Si and S contents of molten iron. Wherein fuel ratio, iron production are the primary indicators of the evaluation, and the remainder are secondary indicators.
The shape and root position diagnosis model of the reflow zone in the furnace is based on real-time production operation data, and the distribution of the temperature field above the tuyere in the furnace is obtained through heat transfer, material balance and heat balance calculation, so that the shape of the reflow zone in the furnace is obtained. And determining the optimal shape of the reflow belt under each operation system by combining the comparison of production indexes, and providing evaluation reference for production operation. The model can perform online self-learning correction on the root position of the reflow belt according to the change characteristic of the temperature of the cooling wall of the furnace body and the change of the static pressure of the furnace body.
The furnace temperature forecasting model obtains a furnace heat index TQ representing the furnace temperature state of the blast furnace through calculation, and judges the furnace temperature trend based on the change of the index, so as to realize furnace temperature forecasting. The influence of the falling of the slag crust at the lower part of the furnace body on the furnace heat is considered in the calculation of the furnace heat index, and the advance time of the furnace temperature forecast is preferably 1.5-2 hours. The model is suitable for obtaining the trend forecast of the furnace temperature change by adopting various modes such as a mechanism model, fuzzy reasoning, big data mining, expert experience and the like so as to improve the forecast precision under the condition of out-of-order furnace conditions.
The furnace hearth state diagnosis model is a furnace hearth state system analysis module, and the evaluation of the thermal state of the furnace hearth is realized by furnace hearth activity analysis performed from the representation of furnace temperature change and slag iron fluidity, and comprises the calculation of furnace core temperature, side wall temperature, molten iron temperature, theoretical combustion temperature, furnace hearth activity index and furnace hearth cleaning index. And the representation of the slag fluidity is realized based on the prediction of the slag viscosity based on the slag components and the characteristics of the raw fuel, and a basis is provided for the adjustment of ingredients and the regulation and control of a thermal state. Based on tapping slag monitoring and slag iron balance calculation, the liquid level position of the slag iron and the slag iron retention are measured and calculated, the liquid permeability of a blast furnace hearth is characterized, the forward running state evaluation of the lower part of the blast furnace is provided, and the adjustment direction is provided for the adjustment of the lower part.
The hearth erosion evaluation model calculates and obtains inner erosion information of the hearth according to the hearth brick lining temperature data, wherein the inner erosion information comprises the side wall erosion shape, the bottom erosion shape, the residual thickness and the erosion speed, and data reconstruction is carried out on the inner shape of the hearth according to the inner erosion information, so that data basis is provided for paying attention to the weak point of hearth erosion. The model is preferably used for deducing the erosion shape of the hearth in real time by adopting an online calculation method of three-dimensional finite element heat transfer.
The tuyere state evaluation model carries out comprehensive evaluation on the safety and the running state of a tuyere, and comprises the following steps: and evaluating the burning loss, water leakage, slagging, blowing off and blowing through states of the blowing pipe. The model is suitable for recognizing the abnormal state of the air inlet imaging video and the air inlet area thermal image video by adopting an image recognition method and comprehensively judging by combining air inlet water cooling and leakage detection.
The key parameter calculation model calculates key parameters required by the production operation of the blast furnace, and comprises the following steps: the method comprises the following steps of material proportioning calculation, material proportioning optimization, material balance, heat balance, cost calculation and Rist operation line.
In this embodiment, the intelligent diagnosis module establishes blast furnace expert rule knowledge according to the operation requirements of the blast furnace, and provides corresponding operation guidance information according to the state prediction result and the blast furnace expert rule knowledge to standardize the production control of the blast furnace user, and establishes the blast furnace expert rule knowledge according to the operation requirements of a cooling system, a material distribution system, an air supply system, a furnace heat system, a slagging system and the like. The blast furnace condition is quantitatively evaluated and diagnosed and operation guidance suggestions are given based on the detection data and the intelligent model results. The expert rules should be fused with the production control standards of the blast furnace users, and the method has practicability and landing property. The intelligent diagnosis and operation guidance is to carry out operation diagnosis on the production state of the blast furnace according to the measurement parameters and the model calculation result of the blast furnace and to provide reference suggestions for the operation of the blast furnace according to the diagnosis result. The standard process comprises the steps of extracting the characteristics of data, identifying production phenomena, diagnosing expert rules and recommending operation guidance.
The intelligent diagnosis about the general phenomena comprises sliding materials, falling and growing of slag crust, forming and disappearing of pipelines, heat load condition of a furnace wall, change of air flow distribution, analysis of hearth state, change of blast furnace state, blast furnace pressure loss condition, activity of the lower part of a blast furnace, judgment of blast furnace water leakage condition, heat loss analysis of the hearth, analysis of blast furnace heat condition and corresponding operation guidance.
a) Material for sliding
And judging whether sliding materials occur at present and the grade of the sliding materials according to the top stock rod information and the information such as the temperature of the top gas, the content change of the top gas N2 and the like, and providing a corresponding dispensing suggestion according to the grade of the sliding materials.
b) Peel off of slag
The occurrence and distribution conditions of slag crust shedding are judged according to furnace body thermal load monitoring and slag crust shedding diagnosis information, cooling wall temperature change and carbon melting loss reaction change information in furnace type visualization, and corresponding regulation suggestions are provided according to the slag crust shedding conditions.
c) Growth of slag crust
According to furnace body thermal load monitoring and furnace body thickness calculation in furnace type visualization, diagnosis is carried out according to the change information of the temperature of the cooling wall and the temperature of the cooling wall, the nodulation state of the furnace wall is judged, and an operator is prompted to carry out operation adjustment.
d) Pipe formation and disappearance
Whether a pipeline is formed or not and the pipeline disappears are judged according to the change of the content of N2 in the top gas and the change condition of the pressure loss of the blast furnace, and a corresponding regulation suggestion is provided according to the judgment result.
e) Furnace wall heat load
The change condition of the furnace wall heat load is judged according to furnace body heat load monitoring information in furnace type visualization, the reason of the furnace wall heat load change is analyzed by combining the edge airflow condition and the change condition of the furnace heat index, and an operator is prompted to adjust the operation system.
f) Variation of gas flow distribution
According to the visual diagnosis result of the air flow, the distribution change conditions of the central air flow, the edge air flow and the whole air flow are judged by combining the cross temperature measurement data, the temperature distribution and change of the furnace body and other information, and the operator is prompted to carry out operation adjustment.
g) Hearth condition analysis
And judging the iron balance condition in the furnace according to the furnace hearth state analysis model result in furnace heat visualization, and providing a corresponding regulation suggestion according to the analysis of the residual iron amount of the furnace hearth and the tapping speed.
h) Change of blast furnace state
The method mainly analyzes the pressure loss change of the blast furnace, the utilization condition of coal gas, the change of the state of the blast furnace such as sliding materials and the like, and provides corresponding regulation suggestions according to the comprehensive change conditions of the changes.
i) Pressure loss change of blast furnace
And (4) providing a corresponding regulation suggestion by combining the furnace thermal index, the molten iron temperature, the air temperature and other information and judging according to the pressure difference condition.
j) Lower activity
And judging the activity of the lower part of the blast furnace according to the statistics of the temperature rise times and the high temperature times of the furnace belly area, and judging the change of the activity of the lower part according to the change condition of the statistical data, thereby proposing a corresponding regulation suggestion.
k) Water leakage information diagnosis
And analyzing according to the change conditions of the hydrogen utilization rate and the coal gas utilization rate and by combining with a water replenishing curve of the expansion tank, judging the current water leakage possibility, and prompting a diagnosis result to relevant operators so as to process in time.
l) determination of the erosion State of the hearth
The current hearth corrosion condition is diagnosed by combining the heat loss analysis of the hearth and the calculation result of the hearth corrosion model, and reliable guarantee is provided for the safe and stable production of the blast furnace.
j) Blast furnace thermal state analysis
Upper thermal state and lower thermal state analysis should be included.
The upper thermal state is judged mainly according to the coal gas utilization rate change and the upper temperature field simulation, and the reasons of the furnace thermal change are analyzed based on the calculation results of furnace thermal index change, material sliding condition, furnace burden descending condition, furnace top coal gas N2 content change, blast furnace pressure loss change, coal gas utilization rate change, air volume change, airflow change and the like, whether the furnace thermal change is recovered or not is judged, and an operator is helped to carry out system adjustment.
The lower thermal state provides corresponding regulation suggestions mainly according to the state analysis result of the hearth and by combining information such as coke ratio change, blast change, oxygen enrichment condition change and the like.
In which intelligent diagnosis about specific phenomena
When the blast furnace state is any one of normal, blast furnace air volume or air pressure reduction, air volume rapid rise and reblowing, regular judgment of the conditions of abnormal air flow, suspended material, large sliding material and blast furnace reblowing is carried out.
a) Flow anomaly
And the airflow abnormity judgment comprises high top temperature, pressure rise and regular diagnosis of the pipeline.
The high top temperature rule mainly provides a regulation suggestion according to stockline data and furnace top temperature and according to the stockline condition and the range of the top temperature.
The pressure rising rule is mainly based on information such as the state of the hot blast stove, pressure loss change, furnace heat index, molten iron temperature, air volume and the like, diagnosis is carried out according to the parameters, and corresponding regulation suggestions are provided.
The pipeline rules are mainly based on parameters such as gas utilization rate, top temperature, slipping material and the like, and if the pipeline occurs, a regulation proposal is given.
b) Suspension material
The suspension rule mainly provides corresponding dispensing suggestions according to parameters such as air pressure, static pressure, material scales and the like and according to the change conditions of the parameters.
c) Big smooth material
The large sliding material rule is mainly based on material rule data, top temperature and blast furnace state, the sliding material grade is judged by combining the parameters, and corresponding dispensing suggestions are given according to diagnosis results.
d) Blast furnace reblowing
The diagnosis of the slide material, high top temperature, pressure rise and other rules should be carried out.
The main basis of the sliding material rule group is material rule data, top temperature and blast furnace state, the sliding material grade is judged by combining the parameters, and corresponding dispensing suggestions are given according to diagnosis results.
The high top temperature rule mainly provides a regulation suggestion according to stockline data and furnace top temperature and according to stockline conditions and a top temperature range.
The pressure rising rule is mainly based on information such as the state of the hot blast stove, pressure loss change, furnace heat index, molten iron temperature, air volume and the like, diagnosis is carried out according to the parameters, and corresponding regulation suggestions are provided.
The operation guidance given by combining the diagnosis result is divided into operation adjustment and system adjustment, wherein the operation adjustment means mainly comprises the parts of wind reduction, moisture adjustment, coal injection adjustment, oxygen enrichment adjustment, wind reduction recovery, wind temperature adjustment and the like, and the system adjustment mainly comprises coke ratio adjustment, additional coke adjustment, coke ratio recovery and charging system change. The operation adjustment should be prior to the system adjustment during operation, and the system adjustment mechanism is started only when a large abnormality diagnosis occurs.
The knowledge reasoning module is used for diagnosing and reasoning the blast furnace according to a plurality of preset state types of the state prediction result, the knowledge reasoning machine for obtaining the intelligent diagnosis result of the blast furnace is a basic framework of intelligent diagnosis of the blast furnace, and the reasoning operation of the intelligent diagnosis is realized by taking blast furnace expert rules as the basis. The knowledge inference machine should select an inference mechanism suitable for the production management characteristics of the blast furnace and has flexible editing and modifying functions.
The rule management module is used for testing, building, editing and managing the blast furnace expert rule knowledge; the visualization module is used for performing visualization processing on the real-time state of the blast furnace and the data flow process of the blast furnace detection data.
In this embodiment, specifically, as shown in fig. 4, the visualization module includes a furnace charge visualization unit, an airflow visualization unit, a furnace type visualization unit, a furnace heat visualization unit, and a safety visualization unit, which respectively perform global visualization presentation on the real-time state of the blast furnace from the blast furnace charge, the blast furnace airflow, the blast furnace type, the blast furnace heat, and the blast furnace safety in a three-dimensional picture presentation manner, and provide corresponding visualization interaction windows.
The furnace charge visualization unit is used for comprehensively displaying the charge surface shape and the charge batch position information of the whole period from the furnace top charge distribution to the melting of the blast furnace in a graphical mode and monitoring the change of the state of the furnace charge in real time; the airflow visualization unit is used for visually presenting the distribution and strength change of the primary airflow, the secondary airflow and the top airflow of the blast furnace in a video stream mode; the furnace type visualization unit is used for displaying the visualization graphs of the thermal load distribution, the shape of the reflow zone, the thickness distribution of the furnace type and the falling distribution of slag crust of the blast furnace by reconstructing the operation furnace type of the blast furnace in the modes of cloud pictures, thermodynamic diagrams and graphs; the furnace heat visualization unit is used for displaying the combustion state of a blast furnace tuyere and the level of a furnace hearth thermal field, comprehensively displaying the heat distribution state in the blast furnace, and displaying the actual comparison condition of furnace temperature forecast and furnace temperature and the activity of the furnace hearth and iron slag balance data; the safety visualization unit is used for alarming and prompting the running state and abnormal diagnosis of the blast furnace production, and the display content comprises water system leakage detection information, hearth corrosion state information, air port safety diagnosis information, distribution chute state diagnosis information and furnace top gear box state diagnosis information.
For the comprehensive evaluation of the blast furnace state, the operating state of the blast furnace is respectively graded according to five dimensions of airflow, burden, furnace type, furnace heat and safety, and the comprehensive grade of the overall operating state of the blast furnace is obtained according to the corresponding weight of each dimension on the production influence of the blast furnace. The evaluation of the blast furnace comprehensive evaluation should consider the influence of the quality of the raw fuel on the production state of the blast furnace, and an independent raw fuel evaluation rule needs to be designed so as to facilitate evaluation reference during production evaluation.
The reader should understand that in the description of this specification, reference to the description of the terms "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described method embodiments are merely illustrative, and for example, the division of steps into only one logical functional division may be implemented in practice in another way, for example, multiple steps may be combined or integrated into another step, or some features may be omitted, or not implemented.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. The utility model provides a visual intelligent perception platform of blast furnace which characterized in that: comprises an application layer, a platform layer, an edge layer, an equipment layer and an MES system (manufacturing execution system),
the equipment layer comprises a basic automation system (BA system) used for obtaining production adjustment instructions and adjusting a production plan according to the production adjustment instructions;
the edge layer comprises a data acquisition module and an edge calculation module, the data acquisition module is in communication connection with the MES system and the BA system respectively, the data acquisition module is used for acquiring blast furnace detection data, sending the production regulation and control instruction to the BA system and sending the production situation to the MES system, and the data acquisition module is also used for receiving production plan and inspection and test data sent by the MES system; the edge calculation module is used for acquiring blast furnace detection data needing image recognition in the blast furnace detection data and performing application preprocessing;
the MES system is used for acquiring blast furnace detection data and production reality of the database module, sending the production plan and the inspection and test data to the BA system, and correspondingly guiding and processing the production plan;
the platform layer comprises a data center module and a database module, wherein the data center module is used for storing blast furnace detection data, statistical data, intelligent model prediction results and blast furnace rule expert knowledge required by the platform, the database module comprises a time sequence database, a relation database and an unstructured database, the time sequence database is used for storing blast furnace real-time data, the relation database is used for storing blast furnace historical data, and the unstructured database is used for storing unstructured data in the blast furnace detection data;
the application layer comprises an intelligent model module, an intelligent diagnosis module, a knowledge reasoning module, a rule management module and a visualization module, wherein the intelligent model module is used for respectively establishing a plurality of intelligent algorithm models of furnace top material distribution, furnace body operation, tuyere and taphole change and furnace hearth state based on a deep learning algorithm; the intelligent diagnosis module establishes blast furnace expert rule knowledge according to the operation requirement of the blast furnace, provides corresponding operation guidance information according to the state prediction result and the blast furnace expert rule knowledge, and standardizes the production control of a blast furnace user according to the operation guidance information; the knowledge reasoning module is used for diagnosing and reasoning the state of the blast furnace according to the state prediction result in a plurality of preset state types to obtain an intelligent diagnosis result of the blast furnace; the rule management module is used for testing, building, editing and managing the blast furnace expert rule knowledge; the visualization module is used for performing visualization processing on the real-time state of the blast furnace and the data flow process of the blast furnace detection data.
2. The blast furnace visualization intelligent perception platform of claim 1, wherein: the intelligent model module is used for respectively establishing a plurality of intelligent algorithm models of furnace top material distribution, furnace body operation, tuyere and taphole change and furnace hearth state based on a deep learning algorithm, and specifically comprises the following steps: and performing iterative training on the plurality of intelligent algorithm models through blast furnace detection data, performing optimization and parameter adjustment on the intelligent algorithm models according to a loss function, and performing multi-dimensional prediction on the state of the blast furnace through the plurality of intelligent algorithm models to obtain a state prediction result of the blast furnace.
3. The blast furnace visualization intelligent perception platform of claim 1, wherein: the data acquisition module adopts an industrial gateway which is compatible with various access network modes and a bottom layer communication protocol; the data acquisition module is also used for acquiring intelligent monitoring signals acquired by the intelligent monitoring module.
4. The blast furnace visualization intelligent perception platform of claim 1, wherein: the visualization module is used for performing visualization processing on the real-time state of the blast furnace, and specifically comprises the following steps: the visualization module comprises a furnace charge visualization unit, an airflow visualization unit, a furnace type visualization unit, a furnace heat visualization unit and a safety visualization unit, and the visualization module respectively performs global visualization presentation on the real-time state of the blast furnace through a three-dimensional picture display mode from the blast furnace charge, the blast furnace airflow, the blast furnace type, the blast furnace heat and the blast furnace safety, and provides a corresponding visual interaction window.
5. The blast furnace visualization intelligent perception platform of claim 4, wherein: the furnace charge visualization unit is used for comprehensively displaying the charge surface shape and the charge batch position information of the whole period from the furnace top charge distribution to the melting of the blast furnace in a graphical mode and monitoring the change of the state of the furnace charge in real time;
the airflow visualization unit is used for visually presenting the distribution and strength change of the primary airflow, the secondary airflow and the top airflow of the blast furnace in a video stream mode;
the furnace type visualization unit is used for displaying the visualization graphs of the thermal load distribution, the shape of the reflow zone, the thickness distribution of the furnace type and the falling distribution of slag crust of the blast furnace by reconstructing the operation furnace type of the blast furnace in the modes of cloud pictures, thermodynamic diagrams and graphs;
the furnace heat visualization unit is used for displaying the combustion state of a blast furnace tuyere and the level of a furnace hearth thermal field, comprehensively displaying the heat distribution state in the blast furnace, and displaying the actual comparison condition of furnace temperature forecast and furnace temperature and the activity of the furnace hearth and iron slag balance data;
the safety visualization unit is used for alarming and prompting the running state and abnormal diagnosis of the blast furnace production, and the display content comprises water system leakage detection information, hearth corrosion state information, air port safety diagnosis information, distribution chute state diagnosis information and furnace top gear box state diagnosis information.
6. The blast furnace visualization intelligent perception platform of claim 1, wherein: the intelligent diagnosis module is also used for carrying out operation diagnosis on the production state of the blast furnace according to the data parameters of the blast furnace detection data and the state prediction result, and providing operation guidance information according to expert rule knowledge after carrying out feature extraction processing on the blast furnace detection data and carrying out identification operation on the production state.
7. The blast furnace visualization intelligent perception platform of claim 1, wherein: the intelligent diagnosis module provides corresponding operation guidance information to standardize the production control of a blast furnace user by the state prediction result and the blast furnace expert rule knowledge, and particularly provides operation guidance given by combining the diagnosis result, wherein the operation guidance is divided into operation adjustment and system adjustment, the operation adjustment comprises air reduction adjustment, moisture adjustment, coal injection adjustment, oxygen enrichment adjustment, air reduction recovery and air temperature adjustment, and the system adjustment comprises coke ratio adjustment, additional coke adjustment, coke ratio recovery and charging system change.
8. The blast furnace visualization intelligent perception platform of claim 1, wherein: and respectively scoring the running state of the blast furnace from the dimensions of blast furnace burden, blast furnace airflow, blast furnace type, blast furnace heat and blast furnace safety, and acquiring the comprehensive score of the whole running state of the blast furnace according to the corresponding weight of each dimension on the production influence of the blast furnace.
9. The blast furnace visualization intelligent perception platform of claim 8, wherein: the equipment layer further comprises an intelligent monitoring module, and the intelligent monitoring module acquires an intelligent monitoring signal through the camera equipment.
10. The blast furnace visualization intelligent perception platform of claim 8, wherein: the equipment layer further comprises an intelligent production module, and the intelligent production module comprises an unmanned trolley and an intelligent robot to carry out intelligent production.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111281461.7A CN114185976B (en) | 2021-11-01 | 2021-11-01 | Visual intelligent perception platform of blast furnace |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111281461.7A CN114185976B (en) | 2021-11-01 | 2021-11-01 | Visual intelligent perception platform of blast furnace |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114185976A true CN114185976A (en) | 2022-03-15 |
| CN114185976B CN114185976B (en) | 2024-03-26 |
Family
ID=80601754
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111281461.7A Active CN114185976B (en) | 2021-11-01 | 2021-11-01 | Visual intelligent perception platform of blast furnace |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114185976B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115287381A (en) * | 2022-07-07 | 2022-11-04 | 中冶南方工程技术有限公司 | Method and device for calculating molten iron flow rate in blast furnace tapping and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101109950A (en) * | 2007-07-23 | 2008-01-23 | 鞍钢股份有限公司 | Blast furnace production process control information intelligence system |
| CN108763550A (en) * | 2018-06-01 | 2018-11-06 | 东北大学 | Blast furnace big data application system |
| WO2019124931A1 (en) * | 2017-12-19 | 2019-06-27 | 주식회사 포스코 | Furnace condition control apparatus and method |
| CN110336703A (en) * | 2019-07-12 | 2019-10-15 | 河海大学常州校区 | Industrial big data based on edge calculations monitors system |
| CN111985112A (en) * | 2020-08-27 | 2020-11-24 | 宝武集团鄂城钢铁有限公司 | Blast furnace digital twin system based on Unity3D |
-
2021
- 2021-11-01 CN CN202111281461.7A patent/CN114185976B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101109950A (en) * | 2007-07-23 | 2008-01-23 | 鞍钢股份有限公司 | Blast furnace production process control information intelligence system |
| WO2019124931A1 (en) * | 2017-12-19 | 2019-06-27 | 주식회사 포스코 | Furnace condition control apparatus and method |
| CN108763550A (en) * | 2018-06-01 | 2018-11-06 | 东北大学 | Blast furnace big data application system |
| CN110336703A (en) * | 2019-07-12 | 2019-10-15 | 河海大学常州校区 | Industrial big data based on edge calculations monitors system |
| CN111985112A (en) * | 2020-08-27 | 2020-11-24 | 宝武集团鄂城钢铁有限公司 | Blast furnace digital twin system based on Unity3D |
Non-Patent Citations (1)
| Title |
|---|
| 刘书文 等: "钢化联合企业生产能源一体化智能管控***探讨", 第十二届中国钢铁年会 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115287381A (en) * | 2022-07-07 | 2022-11-04 | 中冶南方工程技术有限公司 | Method and device for calculating molten iron flow rate in blast furnace tapping and storage medium |
| CN115287381B (en) * | 2022-07-07 | 2023-10-27 | 中冶南方工程技术有限公司 | Method, device and storage medium for calculating flow rate of molten iron in blast furnace tapping |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114185976B (en) | 2024-03-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109063358B (en) | Novel blast furnace forward evaluation method | |
| CN110066895B (en) | Stacking-based blast furnace molten iron quality interval prediction method | |
| CN111593155B (en) | Blast furnace diagnosis system and method | |
| CN111607673B (en) | Pre-iron diagnostic system and method | |
| CN111831719A (en) | Intelligent control method and system for blast furnace ironmaking production process | |
| CN109935280A (en) | A kind of blast-melted quality prediction system and method based on integrated study | |
| CN111639801A (en) | Method and system for scoring furnace conditions of blast furnace | |
| CN102925602B (en) | Furnace profile maintenance method for blast furnace operation | |
| CN114185976B (en) | Visual intelligent perception platform of blast furnace | |
| US20220390928A1 (en) | Production facilities monitoring method, production facilities monitoring device, and operation method for production facilities | |
| Jiang et al. | Polymorphic measurement method of FeO content of sinter based on heterogeneous features of infrared thermal images | |
| Takalo-Mattila et al. | Explainable steel quality prediction system based on gradient boosting decision trees | |
| Kamo et al. | Method for predicting gas channeling in blast furnace | |
| CN116310121A (en) | Digital twin system and method for slag skin of iron-making blast furnace | |
| KR0146785B1 (en) | Error diagnosing method and apparatus for a furnace | |
| Gamero et al. | Predicting aerodynamic instabilities in a blast furnace | |
| Semenov et al. | Decision support system for controlling thermal state of blast furnace smelting | |
| CN103160629B (en) | Method for prediction of blast furnace heat tendency | |
| JP6927461B1 (en) | Operation method and operation system of production equipment | |
| Trofimov | Automated expert systems in blast-furnace process control | |
| EP4170441B1 (en) | Method and system for ironmaking plant optimization | |
| Matsuzaki et al. | Development of visual evaluation and numerical analysis system of blast furnace | |
| Trofimov et al. | Generalized structure of an intelligent control system for technological objects and experience of its use | |
| EP4170441A1 (en) | Method and system for ironmaking plant optimization | |
| Bi et al. | Study on a MES-based large blast furnace expert system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |